CIRM - Videos & books Library - Stochastic control for medical treatment optimization

Multi angle

Auteurs : de Saporta, Benoîte (Auteur de la Conférence)
CIRM (Editeur )

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Résumé : We are interested in monitoring patients in remission from cancer. Our aim is to detect their relapses as soon as possible, as well as detect the type of relapse, to decide on the appropriate treatment to be given. Available data are some marker level of the rate of cancerous cells in the blood which evolves continuously but is measured at discrete (large) intervals and through noise. The patient's state of health is modeled by a piecewise deterministic Markov process (PDMP). Several decisions must be taken from these incomplete observations: what treatment to give, and when to schedule the next medical visit. After presenting a suitable class of controlled PDMPs to model this situation, I will describe the corresponding stochastic control problem and will present the resolution strategy that we adopted. The objective is to obtain an approximation of the value function (optimal performance) as well as build an explicit policy applicable in practice and as close to optimality as possible. The results will be illustrated by simulations calibrated on a cohort of a clinical trial on multiple myeloma provided by the Center of Cancer Research in Toulouse.

Keywords : Continuous time Markov process; dynamic programming; Hidden process; numerical approximation; partially observed Markov decision process

Codes MSC :
60J05 - Markov processes with discrete parameter
60J25 - Continuous-time Markov processes on general state spaces
93E11 - Filtering
93E20 - Optimal stochastic control

Ressources complémentaires :
https://www.cirm-math.fr/RepOrga/2390/Slides/Benoite_de_Saporta.pdf

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Recanzone, Luca

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https://videos.cirm-math.fr/2023-09-06_De_Saporta.mp4

Informations sur la Rencontre

Nom de la rencontre : A Random Walk in the Land of Stochastic Analysis and Numerical Probability / Une marche aléatoire dans l'analyse stochastique et les probabilités numériques
Organisateurs de la rencontre : Champagnat, Nicolas ; Pagès, Gilles ; Tanré, Etienne ; Tomašević, Milica
Dates : 04/09/2023 - 08/09/2023
Année de la rencontre : 2023
URL Congrès : https://conferences.cirm-math.fr/2390.html

Données de citation

DOI : 10.24350/CIRM.V.20088403
Citer cette vidéo: de Saporta, Benoîte (2023). Stochastic control for medical treatment optimization. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.20088403
URI : http://dx.doi.org/10.24350/CIRM.V.20088403

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